Forecasting System Approach for Stock Trading with Relative Strength Index and Moving Average Indicator

Authors

  • Yulius Hari Informatics Department, Widya Kartika University, Indonesia.
  • Lily Puspa Dewi Informatics Department, Petra Christian University, Indonesia.

Keywords:

Decision Support System, Forecasting, Moving Average, Relative Strength Index,

Abstract

Stock is one of the investment instruments on the capital markets which provide high returns. Furthermore, Indonesian government also supports to raise awareness on investing in stock market through the national movement “to love stock market”. Apart from that, a lot of people want to invest their money in stocks hoping to get a big return in instant, however many of them suffered losses, and the intention to gain their money is not achieved. Lack of knowledge such as “high return means high risk”, is often forgotten by community. Therefore in order to increase the interest of the community to develop their money in stocks, ability to analyze on stock transactions is deeply needed. This can be achieved by using indicators as tools for analyzing the stock transaction. Departing from the elaborated issue, this research is expected to help the community in analyzing the stock, to know when the time to buy and when the time to sell. This forecasting system will provide an advice to stock investors and stock traders to pay attention to indicators: relative strength index and moving average. In conclusion the result of this research can help the investor to determine the right time to buy and sell. However, this system cannot predict very exact time and cannot became a standard for profitability, because the volatility of stock price.

References

T.A. Napitupulu and Y.B. Wijaya, “Prediction of stock price using artificial neural network: a case of Indonesia.,” Journal of Theoretical & Applied Information Technology, vol. 54 no.1, pp. 104-108. 2013.

A. Rahmadhani, M. Mandela, T. Paul, and S. Viridi, “Prediksi pergerakan kurva harga saham dengan metode simple moving average menggunakan C++ dan Qt Creator,” in Prosiding Seminar Kontribusi Fisika, Bandung-Indonesia, December 2011, pp.178-185.

M.T. Leung, H. Daouk, and A.-S. Chen, “Forecasting stock indices: a comparison of classification and level estimation models,” International Journal of Forecasting, vol. 16 no. 2, pp. 173-190. 2000.

R. C. Cavalcante and A. L. I. Oliveira, “An autonomous trader agent for the stock market based on online sequential extreme learning machine ensemble,” in 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, July 2014, pp. 1424-1431.

S.S. Lam, “A genetic fuzzy expert system for stock market timing,” in Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, May 2001, pp. 410-415.

R.A. Levy, “Relative strength as a criterion for investment selection,” The Journal of Finance, vol. 22 no.4, pp. 595-610. 1967.

L.A. Teixeira, and A.L.I. De Oliveira, “A method for automatic stock trading combining technical analysis and nearest neighbor classification,” Expert systems with Applications, vol. 37 no. 10, pp. 6885-6890, 2010.

Agrawal, S., Jindal, M., & Pillai, G. N. (2010, March). Momentum analysis based stock market prediction using adaptive neuro-fuzzy inference system (anfis). In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 1).

T. T.-L. Chong and W.-K. Ng, “Technical analysis and the London stock exchange: testing the MACD and RSI rules using the FT30,” Journal of Applied Economics Letters, vol. 15 no. 14, pp. 1111-1114. 2008.

P.-C. Chang and C.-H. Liu, “A TSK type fuzzy rule based system for stock price prediction,” Expert systems with Applications, vol. 34 no.1, pp. 135-144. 2008.

Gunasekarage, A., & Power, D. M. (2001). The profitability of moving average trading rules in South Asian stock markets. Emerging Markets Review, 2(1), 17-33.

Appel, G. (2003). Become Your Own Technical Analyst: How to Identify Significant Market Turning Points Using the Moving Average Convergence-Divergence Indicator or MACD. The Journal of Wealth Management, 6(1), 27-36.

Hari, Y. Darmanto, P.D., Lily (2014). Customer Mapping for Cable TV Industries in Indonesia Rural Area Using GIS. Jurnal Teknologi – University Teknologi Malaysia vol.72 no.4.

H.V. Laerhoven, Zaag‐Loonen, and B.H. Derkx, “A comparison of Likert scale and visual analogue scales as response options in children's questionnaires,” Acta paediatrica, vol. 93 no. 6, pp. 830-835. 2004.

Brännäs, K., & Shahiduzzaman Quoreshi, A. M. M. (2010). Integervalued moving average modelling of the number of transactions in stocks. Applied Financial Economics, 20(18), 1429-1440.

B. Egeli, M. Ozturan and B. Badur, “Stock market prediction using artificial neural networks,” in Proc. 3rd International Conference on Business, Hawaii, June 2003, pp. 1-8.

K. Aghakhani and A. Karimi, “A new approach to predict stock big data by combination of neural networks and harmony search algorithm,” International Journal of Computer Science and Information Security, vol. 14 no. 7, Jul 2016, pp. 36-44.

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Published

2018-05-31

How to Cite

Hari, Y., & Dewi, L. P. (2018). Forecasting System Approach for Stock Trading with Relative Strength Index and Moving Average Indicator. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-3), 25–29. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4188